{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据挖掘理论包括：监督学习，非监督学习\n",
    "## 其中，监督学习——根据已知数据构建因果关系模型f（X）=y，从而进行预测"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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KQflpF2NkqZnMa4dxPKQdXtrjPDefOGJ0L/T4i/LNAtJHT0tWIQ3yltXOId/8jvrIM3GKJGFjxEmSOEsSp0lph+T5/amP1UfSSlgZ4x24f8XtjIWaBjDhuQg52T+atEroDdqymbNd/YMYzKXP4V/e6TjmSGhf1COzcqqfSZuSw8zcWLsYlY2mrhNod/bLSv5giJao+uLveTyuV+s/AFHOWlwPxvEFqSce5+hVTlNO1B8jcfk+bl0D+imzPtlW1qHCssSZ64iiYOKmZmEg5d+R4Dk5pWt83IsJeDPuphPG2YVYQGxFv0c3+PcxmWSOCVnypFS27QGeQhe/MiDKXHPLGIxdPBUXtooH6jmoxC4nHEoD+h0/AW3RYf023O0K4f2rvt+j1ae8T2ueRy3WoNlS6INlmd94/1vS5WcwL69JgARyJ9Ab5Jue2NNjGDNJn9RTNR54dDn+JOa1ytrCG6sk5TPPjDIqzuAkprpZRuo3aYPy/K8nzryoA5HKqSnM+qZ8s2D0odNeo5AGmctq59dHTVIfefa//ue/KwVVx0M9mklBlTyXzx5WH1O+WUG9uWo8FVQDJcO3UTTikoryp50cJZvk3TiwEhIGRa1Uzp1orZhG59ODx7hak+9HCr8EZUn6OMKsjthFR+zBsB+bc78CbO76v+06bDfs/lTRV/6BbwqvhDbnsoLj7qGNKy9CoTV/pIT/XSO9uKvRveLdvkTAnXE3nWpZg/eMc7KuOE3PaDbumvVWDEVUEDHzf1PM1YcN7HT2kA7FiIf0JM21U9txvHMOau8ditMt+yG++AZJuxP2rxrZaMJXGfMzZIxdatL7Q1nFh3ISOaCdyyWb+ZpXINsc8nYC5RXCMxIoewKlLt+iIhPEvTTbKkUm4Pq37MJVZNvSY+c1W39ELgUPs8fUyF09Rooeu5i8lG+GRN/77rUKafBVyGrnrSPuVB/zTCunR9xQM+Z+0rfK034Up/c1qGRi1iv7UM1KqtTDIxsBV1mR5GovKHBs7Rov1EhiMUGzO7mGLzC8rHbsEdO5mjmobNnlmc8mltu1h2ZmzldKy0FcmjsxdnXHpA0qwO5A0yRwvo3LdRH+ss9reNBRUsRg2rfy45hBBor1X6rZzmTB788QvnL5y368mH0oiGirmCDOuOUR7HEnKaTsrIPncDt4p/cQEOVs3OI5aLdMd2XyqctxL12FUDsKytVLoz2AMmat1z93vM4qp0lDMOyhBpzf/CLGProBtw44hIs4gWtXANiKdMT/97i3k2WCype3ZQ2OjgrEDvUl0Beet+5kD8BhRTmiMN4iARLITKBU5Zt0wCh9lYsb0G//dLxruZJI6+DVS0MxY91eXJO9nS1zULsYaN7vDz1nynC3CwTjRJsEFVMx2JaZANR2ISeUl0QyuHhmFQbF+PugfDMg+953n1FIo16NUSLNMzHrVftQ24+q1VRZIU07JI98zny0TSUVx0iioA6s1EqqrNTy8Aj4V+m8+2I6JitvaeaaWRUrpczJoNYoui1OPE2vyoKfSdu046Rg0f5QMMGnOV8PqFCxOq/mkDFpD4ddjKdQJyujvvcYcAAj5bnvyXLZbtejvaZqM8o9a/VeETNTKun8M6PJbbHL5XnvJxBn2jY/blIjpctKmTTB1V1z1ulqgsool6YI9/+/45lX7nvKqGeOpu69Y5yYmdwXcG3QYtwxQPIMUR5qjzuON/T+eRlZaY+/Jkfkt/v79upT7XAdkPl/D+5vLcMebfndakdr8SbJdpvMIFV+mzNu2e84PLJT8JwESCAXAqUr36wwedV73T3u9t/64KS5kQ8SV1627dwkezudeKLzAwqjK6ciwu5pGSumwvGOG7OMYSjfcvmf2PvS9mmFNPg6TCzU6rsWqUeiaMr+U9mHKiujssc07TCxUM/9aodKKgpq/4rRuNmJhSp7Xcv1MMJL918GW48D7yyFhAOxD3cGLULRsdMlnStHOu7KXNgUJClvPs98g14pwAx83birL+KcEwomn/LtPMGVC/tZ1Lk3oI56GncvuAIdl072kW7Cns3ADMf1u/oD5kwGJCkRMgHRT7xDyfuPUFxlUuG0zLZmWdFNaB4f9T4Cfq/VAFrW4MAt2WITh3rrmuuOwdhJEzFowF7Mm6S9S2svuPr/mIShunYplFtNlIX2p5oyK8ag/yVZCRiCi5tlQLUcIzARN5n/28o6Yohbbm57Ni3zN1+zcvhtWvl8g75HA3EFnXTBbQDeysQa7HHSJPkBsKrjKQmQQAyBkpdvdrut/e9DRnp7zkWeGHmZ5j3fHdNF/J23q5LtWFH+MUwaM5aJCtVH+WYo9d3vslJIg6/RKKjDv7VAPRJl82rnSXzWcVStpIqX3rTDKKjth3eqpBK6Rjz4DnQUVPEWXC7HrXMbMPbSUhzHcqW83ChxSCM6L6tjtX8Q0w9x+d2Rl0JiVtik+B7x1HpG9o6ZzuiB760DHA906rYOBRMXlsbk7I7vrHs+bRNhbbYjkwW5K4S5KsxRfbbbEjTFjkrPe32HgJrQeKUDtz26XMXANZYAdnzfrL1VgxSzklnjmagqr5duqBlj7r/QLTaoOLoDKjeFc+KEfHFX+eF55711lISq2oWLzUPxJyUbcjU7z3GFNNg269oM5I53+ss0SZTi6SjpMiA8bk0GysrEvHWTcWytdiw1vHznVA0ufpNA3gRKUb4ldsbd7pB//GZ7PBZd1wVcc8ZPQasVk96TYTKJvRR2WDDvGeWb4dUXv8taIQ2+UFntlI+JhSqxT+1YqBLnNO2QPPIxsVBFIbVjoYrC2lePoGKUpCiawZzsF2jeOTkxxlWxebkrgk5DvNAyASdHLVtxbtJEdMeATgSyma3MZBYYAc0NhK327A3BrQOX48Irm0J7cyOyxt5SbNYaz8OxydQK2LuWV2KVUg2KK3BW9hVvHuquwCaUwke9nIAeWDgedQcA/Wbv1/vAVb9OZIrd6UPgThTpSSL7WTgUgzcostPJuT2g8v5Pe4Mf1/rDXgGonqy97zY6JukRe6OC9ahrd0Ui8ml+N9WKrtfe5EL8ZnP6nchveA5qqYwmo+NTEkggUKryLa7Jqr3Odod8LSM8eRlXi/9+ZOxmN4mWYRL+rnn/Doyo1jHlQfnmEurLJ1RIE96uKI/yMQqqKKS2giqKZ9oheT79RD67VVIx6bUVVFGAy/Iwg7le0Hl3E71qqz/OajAUzPF3dmCwY9paSl3T5rG6RW64lwELcJcTGzLfVcq01dLIgXwATNq+4kByXvZiApd3vgjcu8GdtLmx9nGMPW6Z9Xduwm+aazEl6Mgros/2RJE3SeRPaO9LUr8B53GSCZr5vZsyvd+OX+mV//uDa+CGfzL7rPwtSLrKzWQ3qXw7Dqn7m4utOqlev+O42CL4gARIIESgN8g3X6PN1oRIp4JmAi/ZC3faGEDqMzLJeNb/SMLI2JN7vkZJTPr4OKSmrGAW75ryzWPRu86okObwvmS18xsT5DNL5RKTXuUkqeOoMvUV8920Q/Jc+O0v1UfSilMkE2pGTH3FjLjvHEaghXskQkyFUDk+Nd5Dazhbt95Rm/L3+1fqZDD6G/Hk6xzBWUS3HyZN5yacPbMAdwQ2/Jv8+X97LIPmhmllqoG7/AGQhIHwLMpcbzHw3la9by3o1CCtbD4ngawEZCBxUbzEWqtwYlUx7KHncc1aZc9iuuut6un/01kUWK+d8QMsKffiH6aidjbQ3OjFx5O8RkE15Uha12oBQJKSa/LIt6082vdF1qiBmqxUBvZVa4chS3EA6ftsxTzXOB6JLj96JVWvdGz1QtnYmXlOAiSQSKA3yLdgB1yrKWu7g5umQJYc3oSensy/AWYifA3exfM5W8dRvrlvqM+dUCHtwiuV1c4h3/yO+kgx4hRJnCOJkyRxliROk9IOyfP7Ux+rj6SVsDLGO3D/itv7dCxU20zOcHLjBJobPfRte4izHfgo50luG/yro+Z2cJWn3TY1MYkAxId58IeP8ExgvIGjWbWR4rIOfCWtN8iNdiwkaWyl1G1j0uyl1adCnJr9e1SGC0Gz9Mrw/X+eFG5faFIno+MLtac7h/+n/t9yuB1yR207mCs+BRZgXu0hFaaq3Umqfxveb1LH9BuDSrVKegJiipYWbiZ9dl8qi3dqJG3IopRG9y75rl7pmJiciE9JgAR8BHqTfLMbLmMeFTXAdQ5pP7XP899bqtg4k+GuZZYz5pgxux17Gtfg3RZPptq1Fvqc8q3QRAtfHhXSAjKV1c6vj5qkPlKshJUxcU2VqW8GBVXyXD57WH1M08wK6s1V4/ucgho7QMshnp/hJN+uQmXfjLkfl9bnVbZ2A+bXBgoLXAb3zgYeu5dBhcunLLqpZFA8EePWNaDf5qXKI91x61nWmI16BcmYQqYLfM+xibMfVMUptSq2nKBYd7t8apRRKag7B9tdbigLyJmA//+gZDemVN4KYDiNV43aaxRQOO30wd+TlzN8JgMjHQ4FQIa9nt7/S++3Y+4d/3gHcGmTdnJWsxh3zJ2IESqurlZK2wNttlsTN7svaTx54PGx83b3ucvWcUonYR54kAAJRBNwfy+us8PeI9/cCfiEv+ve6unk1BjrUYQ8RV2sSxpCVmOyIDFv4MvKOks5WouLWxpVeB733PdF+ZYHvZ7JcsP8dXuTfM/0TCvKqBaloLYdcUPN5NN1MeuVfahmJVVWVUvxsAVSVNgPu82usEjxxGbnMed2PbkMUk3+7vz2+uUNbE19egDagdsC5nnmuXzbfRNlNGjKZ6dV6V2zP7kK1xlMH3XtDYzlabhOf5uiSvDfq6yZg/aEOLHmnekBPzD2UW+Pob+kbFd/M7VKJfyH/W3ZMjBVryCQ9FuyO+Cls+9653FeHiWFO1CLiKWnnru/r/BvyyisXk3h3473LHzm/e7i83kTeP76vbzhcvO9k8Qp3zKZr+sEKN+6zrAUS/Dklv+3HWyrly74RF+n/W5dORWYOIuTIWnlBVvhb19yXyRv/HjCk4NxbQvWnct1rv3KpWymzY8AFdL8uBUsl5j1qn2oTixUWSHN9RDHSKKgDqzUSqqs1JbCYQRNmhlbV9vaU/V0tZ3dld//B0BWgHTYnXJd4eCArbv+pxWv3DhFLK1F/t9G9ODInyZs2h56nvD70gpte2hSxR0EpjS4cvHzwNZkr9VuyKZBue+/Sqmej3sBAcq3XvCScmxid8o30xSvDr1iGbfv3oynZKuCmSw2ZUR9h+SjShQta6Pym3ue0pl7XlMGv3s3ASqkJfb+REGV/aeyD1VWU2WPaa6HKKj9K0bjZicWqux15UEC5UKAA7ZyedPsJwmUHwHKt/J75+wxCZQDAe4hLbG3LOa48hn+LXGsAYjn3qudJ/FZx1G1kipeetMOySOf9sM7VVIJXSMefAc6Cqp4C+ZBAiRAAiRAAiRAAiRAAiRAAsUmQIW02G8gpX5Z7ZSPiYUqsU/tWKgS5zTtkDzyMbFQRSG1Y6GKwsqDBEiABEiABEiABEiABEiABHqaABXSnibexfpEeZSPUVBFIbUVVFE80w7J8+kn8tmtkopJr62gigLMgwRIgARIgARIgARIgARIgAS6mwAV0u4m3M3ly2rnNybIZ5aqSUx6lZOkjqPK1FdMd9MOyXPht79UH0krTpFMqBkx9RUTYh4kQAIkQAIkQAIkQAIkQAIkUGgCVEgLTbTI5clq55Bvfkd9pCniFEmcI4mTJHGWJE6T0g7J8/tTH6uPpJWwMibETP+K2/tcLNQ0HnxOAiRAAiRAAiRAAiRAAiTQPQSokHYP15IpVVY7vz5qkvpIoySsjIqFKl58245mUlAlz+Wzh9XHdMysoN5cNZ4KqoHCbxIgARIgARIgARIgARIggZwIUCHNCVfvTyyrnbeOuFN9TG+Ugtp2xA01Y+4nfRul9vS+BpVMzHplH6pZSZV6eJAACZAACZAACZAACZAACZBAEgEqpEl0yuSZUSJNdzG7JQQAACAASURBVMWsV+1DdWKhygpp2iF55HPmo20qqThGEgV1YKVWUmWllgcJkAAJkAAJkAAJkAAJkAAJ2ASokNo0eK4ImFio1XctUteiaMr+U9mHKiujssc07TCxUM/9aodKKgpq/4rRuNmJhSp7XXmQAAmQAAmQAAmQAAmQAAmUNwEqpOX9/jP13iiow7+1QKUXZfNq50l81qH3oYqX3rTDKKjth3eqpBK6Rjz4DnQUVPEWzIMESIAESIAESIAESIAESKC8CFAhLa/3XZDeymqnfEwsVIl9asdClTinaYfkkY+JhSoKqR0LVRRWHiRAAiRAAiRAAiRAAiRAAn2bABXSvv1+e6R3ojzKxyioopDaCqoonmmH5Pn0E/nsVknFpNdWUEUB5kECJEACJEACJEACJEACJNC3CFAh7VvvsyR6I6ud35ggn1mqPWLSq5wkdRxVpr5ivpt2SJ4Lv/2l+khacYpkQs2Iqa+YEfMgARIgARIgARIgARIgARLo3QSokPbu99crWi+rnUO++R31kQaLUyRxjiROksRZkjhNSjskz+9Pfaw+klbCyhjvwP0rbmcs1DSAfE4CJEACJEACJEACJEACJUigaArpdVzA+c1LcbwTQM3zmD93YsHxXP98Bz56ZROuYgzGProBw4vg2PX6mZfx3tZdQEIbruMQjq1dg/aENF2B80XzI9jTeEIVUbl4L+6o1qWZd3BhbAOm1A7pShW+vJd2TsfpW6LLlDo/3Wq99/+2HBJWxsQ1Vaa+GRRUyXP57GH1MZWbFdSbq8ZTQTVQ+E0CJEACJEACJEACJEACJUzgz0d9Z9n/WZz2nULnu3vxR6n8Xy/iP9wxGwO+WtiW3PDV23HzV3+D82dP4PKvG3D5qzMw9D//p8JWklLal2e24fzZPwD4Ay7/az9U/Jfb8RfBPJ83o/XXh/FvkubXv8mLhVK+X/g+juxrwOlrozHqNk/B/B//0ui0AfjjVasNZzbjo4Nn8G9n/zGUJ9jErNeijDa3QJUZzfv/wdX/6x9x+f/23vvAm/4cNw0YgluG/RcM/au7Meo7yyBK5Y1/OQg3APjyj5cyVS/pZNW188hunN7XgMtnW/DF1X/F9f/1/+Gr/+kW3PBnf56pHCbq3QTGVvZXHTjeoaRL7+4MW08CJEACFgHKNwsGT0mABPoMgaKtkOLMQbQLxorlmPHQAtzUTUhvHDkV/RtP4CqAq3+4AMBT1KRKszr5p9nRK3pdbda1P+iVyaRV4C9b96v2SV2Vi/Nbyb1hwALcta5Wrzq3rMG7LVGrwnNQ67BWq6P7ZeXWOVq24tykibGryGY1Va1omzwx37IKO+KKrMpux6Xaybi4dg2i+Paf/XhsfcYc11QhZr1qH6oTC1VWSNMOySOfMx9tU0nFMZI4ShpYeYdaQZV9qTxIgARIgARIgARIgARIgASKR6BoCunlU44Z671hZdQoP9emeualQURGkWzHHNSuW4VBwQTmekAthlRswtXOOagNmAV7ZQBoXIoDKKxSKv24phb3xmDspGiTZElz8bijtFYsxwjHnNY0P5fvGzAEw+5djgvKTPkEjr+zA4Mf0rFDg+V82fyiNpfOOCEgZQ9/aC+GOwWZlVCjaLtmwU4fbqregPm1WuGXIDBXG5fi3UZ5VwuDTcl0bWKhVt+1SKUXRVP2n8qKqJj7yh7TtMPEQj33qx0qqSio/StG42YnFqrsdeVBAiRAAiRAAiRAAiRAAiTQcwQKppC6CkpU2yvmoBK70N4pK4B7Ma76EC62AHErZK6ytHV65Mqav4pdaF5rrfT5H1pXcem0QttP7bNciqMD45Vgq7DYU6NMH8dyzLgXuNDp9dN91mmtXn7erNJIgZVT/cq5m35Q9j22slJ6W80mZTbbf2ytWnn+ItBaMe/9jewpDSijes9tB25LUvADZeVyWbl4FW4VJT2XTDFpjYI6/Fta4RZl82rnSXzWcVStpIqX3rTDKKjth3eqpBK6Rjz4DnQUVPEWzIMESIAESIAESIAESIAESKD7CBRMIXWb6Cg5N7qOekTRWoh+jnlo+9bp2lRXMqhVMzdn5EmUmW0wYf9CmNvW6hW9YNl5X3duwp5XnNyhfp7A8VceAR7dgH4fi9MlffjY2BV3rsG7yK6UDpq7F/Pn2gXY54dwTFZQg8qo63wJaN48tItm1Idw1DHTvavWrrv7zmW1Uz4mFqrEPrVjoUqc07RD8sjHxEIVhdSOhSoKKw8SIAESIAESIAESIAESIIHCESi8QprSNtvLa1xSzzNt2Mw2Lk8p3g8qykGvv8MGHMKxFt3yKC6uGawkudSJLzDR3WvrlZVLz61VYlGY127yZ655HvMmdSrPxL9prs3T8+5+/Gat3rMrEw4fYTm+5q+lR65EeZSPUVBFIbUVVFE80w7J8+kn8tmtkopJr62gigLMgwRIgARIgARIgARIgARIIH8CBVNI+90yBoCzFzL/9hQ0pzIjvhTvNMlVfLsp7ExaZ75s3qpXi2ued0OxROfxnBGZ52KaO2Vd9P5QSePbH2syme/A6qi5rb8nhsp1TYclRE/wUA6UrJudoozaZtDeCrCVqsdPZbXzGxPkM0vVLSa9yklSx1Fl6ivmu2mH5Lnw21+qj6QVp0gm1IyY+ooZMQ8SIAESIAESIAESIAESIIHsBAqmkGavEtD7Tf3OiMxqoKwq3jUwl9J02qgybx01B2jZhD07KyLjnH55Rfn5BZRS5W9P7i3IMccVax9nwNlSjiWFkvuU0ZrnMeOWrU4c0jmofXQoTr9ir45a+1lDJekbQYdGtoJqVoHN+/PtS63dgHm1JsZqTOFFui2rnUO++R31kSaIUyRxjiROksRZkjhNSjskz+9Pfaw+kvbPvvIXSkEVJbV/xe2MhZoGkM9JgARIgARIgARIgATKnkBRFFJN3TIftV7D1cYXcX52pXUn2+mtk5ajf8smNO+c7Cmf1QsxtmIXjreswdFRYWdFbkgWFW7F76nXdtJklC67JX6T2dyV2Qv7ZeVwDMZGeBm268n13NcuZ+X3i+atXjHWyqruo+xnnY7jkiLrSrHliOlrA/1hdLyKeteZrHZ+fdQk9ZGWS1gZUVDVp+1oJgVV8lw+e1h9TO/NCqrEVZVzHiRAAiRAAiRAAiRAAiRAAh6BIiqkngLkX3F7HMMGbtcKktfO1DPPu6ynfMrK3uCxY3C88wTat76MwZb3WFlFFE+/6ogwmbVNkK+qeJp+hdWOHYqKocgtouUJXFUeh8Wp0fSYmKGpXQ4lcE2QlYIdVsCDGcT50by51gqmrBQnmDib/G7fcwlT4zC+bgop8W9Z7bx1xJ3qY5qqldMjbqgZcz/p2yi1p/c1qGRi1iv7UEU5lY/Uw4MESIAESIAESIAESIAEypVAcRXSAlPXJrq7fMrnjSOnon+j7GvchdPNCzGo1lnR+7wTf3LqrxwVjhHq5ZNEu3DxzCoMcmKEigLtxg4FYMKrZO+OZyZ7vfp5VLasUauU1xb7lUh7BTep7KCJ7vyACbApR8KuBOO13oCJGLeuAf02L9VxSTs7VFiWm2IqtPseDFMTlUXKv2PdXvSFtUGjRJp+ilmv2ofqrKTKCmnaIXnkc+ajbSqpOEYSBXWgo6DKSi0PEiABEiABEiABEiABEigXAn1CIXXNRqsn63in2OWZ7g6oxZCKTWpF0l7pTFvl81Zc9X+F9lOHcEe1UVwv4Jrr4GcMhoyMNlu9Ggr3Ev5vJQrbiNlj0N4oq7iPoN+jGzB8QDhd1B2fIuo4EgoqnLK3s1lWgiNWgU2ZwT2i5n7UtxsjNq68CIXWmD+r/cEjvbirUeX3pnsmFmr1XYtUs0XRlP2nsg9VVkZlj2naYWKhnvvVDpVUFNT+FaNxsxMLVfa68iABEiABEiABEiABEiCBvkqg9yqk1gqneTmi3A2uAdpFAWvZinOTJmL4gCHoJ1qaUiD1Suet1d4KZ9LqpllxVeW3HMSluRP1CuOZg14s1ZrFsQpkcO+pb3+naTQAbzX2BC60XsBws4pr0kSYBHvmuRn3rxbAcZPUuafxBJTjosAqrGlqlPmymAbPuOUR7GlcivcaTcp4Rd6k6G3fRkEd/i3t/ViUzaudJ/FZx1G1kipeetMOo6C2H96pkkroGvHgO9BRUMVbMA8SIAESIAESIAESIAES6CsECqaQGrPQngczBv0sr7yeEukpd/Z+UNU+1ynPHNwWVP7sDrgrrnLTM9u9fGqXmyrK3Nd9mPVkQIWK1Xk1a3oAN1Svwvx1q1JzeO8lWXH1rbZGhIVxFeAYx0c3KY+6F3B+81IvvqmVVp7Pr432sJzaiV6aQFY75WNioUrsUzsWqsQ5TTskj3xMLFRRSO1YqKKw8iABEiABEiABEiABEiCB3kqgYAqpC6DTDini3o0+CcawjE4VffdKB7QCVwmfVePAoegPqGdX/3ABwBBrBXIOBlcDXzbvV8/7z14Y2lNpV2ab08r99v07MKK6wnOGBF2enSef81z3WdpOoLLXF+3VODK/vMPNwIyHFkD2kipldeuuVC+8YvrrrkZbyqipQ1aIT7ckK8YmbV/8FuVRPkZBFYXUVlBF8Uw7JM+nn8hnt0oqJr22gioKMA8SIAESIAESIAESIAES6C0ECq+QOqtrN8Lz3vqnKxeivdA6SoutYCkl0lrxjAPpxhANmrNaq41m9VL2g05Zp80oVV3HTwDIZjLqmdOK2e9+XGyutMx1JycqtHFt7+r9rHs+bRPhysUN6LdfHBflrhDmqjBH9c9uS/NaoNbyeByVvhzuyWrnNybIZ5bqrpj0KidJHUeVqa+Y76YdkufCb3+pPpJWnCKJ8yVxkiSmvmJGzIMESIAESIAESIAESIAESpVA4RVSp6e2EqOVQD8Ce39lUMG6fuagP3HElTFFDe4BteuNyAYYc92EvZ++fJZTJOAEjsseSucwCq+5LuS3MLt2ySlxUIVaqcy1/MsfS6xTOWQldwhuHbgcF17ZhK4ohD7T3qQGRa1+q8mKCpxduwbNm4e6K7BJxZTTM1ntHPLN76iP9FucIolzJHGSJM6SxGlS2iF5fn/qY/WRtBJWxngH7l9xuzpPK4PPSYAESIAESIAESIAESKCnCBRMIRXHNfPnZmu2pA16g82WU6cSpUjHEM22ymmXbZS0rMqkKMsmlqldjlby/HdK6Uqbx+oWueFeBizAXYs78N5WMd/Nb5UyTeE3HnXFq28w/Izh01fCwJj+dNe3rHZ+fdQk9ZE6JKyMiWuqTH0zKKiS5/LZw+pj2mlWUG+uGk8F1UDhNwmQAAmQAAmQAAmQQFEIFEwh7dHWGy+3WVc5nca5imzFcoxwY4oa0+J4U9YbaxejsnGNZ6or5dV0t7muF1qm/y3RYWXimKtVzFec1dFAeBZxhjRvMRyldBfsleq48ni/NAjIauetI+5UH9MipaC2HXFDzZj7Sd9GqT29r0ElE7Ne2YdqVlKlHh4kQAIkQAIkQAIkQAIk0BMESkIh1R5c9YrdrbiA8/s9L7ZRELSX2zEYO8nEBY1KFXHPUWQrp1rOetYaRVNWDdsxNiIOqKwKuuFknGKzrLDGxyE9gWtXADghJj0z2DFu/V82b3UVYDfOakSXgrfsvZpxq5S2Uuq2MWFFM1hHV68lNqqEj6Ey3FWScJVIU5KY9ap9qE4sVFkhTTskj3zOfLRNJRXHSKKgyj5UUVJlpZYHCZAACZAACZAACZAACXQHgZJQSKHCq6xB89qAIhp0WCQeX5WnVlmhXIxhAw7hqKtQZsfTvnW6q+yJ6a042FGK8OalOP7xIQyPiLHphZORerJ51w0qXD5l0WquKLzj1jWgn9T/ynQct55lrct2DCUOm6IUa7tYpZSum4xjhl9wz2dE6Bc7f77nRhmV/KIMH0ADpiSF3sm3ojLNZ2KhVt+1SBEQRVP2n8o+VFkZlT2maYeJhXruVztUUlFQ+1eMxs1OLFTZ68qDBEiABEiABEiABEiABApBoIcUUs/8NKrR9r5E32rhvXol0+RRStc7Yoo6B7VzJ+IGAIXbjzgEwx/ai+GmssC3HXs0m7lu3P7WaNNg49hpmOWd2CjLSftt/UpuuiJqd8twH/f5DnxkTHwdBd2u03sndu7488qaOWgPKrhWcqOoa+X0RZwbuQHDqeNYhAp3ahTU4d/SXqZF2bzaeRKfdRxVK6nipTftMApq++GdKqmErhEPvgMdBVW8BfMgARIgARIgARIgARIggXwI3DB/3d7r+WQspzx+pQ+oXLwXdzh7UIvBwb8aCqCbVjOL0TfW2bMEJPapHQtV4pzmeohCasdCFYW1mMffTK1S1f/D/rZiNoN1kwAJkEDBCVC+FRwpCyQBEigBAj20QloCPe1CE75s3e+ET5FCspnrdqG61KxmNTVuNTe1ACYgAYeAKI/yqbxTu8gWhdRWUEVhTTskz6efyGe3SiomvbaCKia/PEiABEiABEiABEiABEggigAV0igq1j1Zjbx43Is9ms1c1yqApyTQiwjIauc3Jshnlmq1mPQqJ0kdR5Wpr5jvph2S58Jvf6k+klacIplQM2LqK2bEPEiABEiABEiABEiABEhACFAhTfl/wNXIFEB83KcJyGrnkG9+R32ko+IUSZwjiZMkcZYkTpPSDsnz+1Mfq4+klbAyJsRM/4rb1XlaGXxOAiRAAiRAAiRAAiTQNwlQIe2b75W9IoFuISCrnV8fNUl9pAIJK2PimipT3wwKquS5fPaw+phGmhXUm6vGU0E1UPhNAiRAAiRAAiRAAmVAgAppGbxkdpEEuouArHbeOuJO9TF1KAW17YgbasbcT/o2Su3pfQ0qmZj1yj5Us5Iq9fAgARIgARIgARIgARLoewSokPa9d8oekUBRCRgl0jRCzHrVPlQnFqqskKYdkkc+Zz7appKKYyRRUAdWaiVVVmp5kAAJkAAJkAAJkAAJ9H4CVEh7/ztkD0igpAmYWKjVdy1S7RRFU/afyj5UWRmVPaZph4mFeu5XO1RSUVD7V4zGzU4sVNnryoMESIAESIAESIAESKD3EaBC2vveGVtMAr2agFFQh39rgeqHKJtXO0/is46jaiVVvPSmHUZBbT+8UyWV0DVT/tvPlEfftLx8TgIkQAIkQAIkQAIkUDoEqJCWzrtgS0igLAnIaqd8TCxUiX1qx0KVOKdph6yy0ow3jRKfkwAJkAAJkAAJkEDpEbjh+vXr10uvWWwRCZAACWgC7e3t2LdvH37961+r71OnToXQfPe738U//uM/hu7zBgmQAAmQAAmQAAmQQGkToEJa2u+HrSMBEggQuHjxolJM9+/fj+bmZhw5cgR/93d/hx/84AeBlLwkARIgARIgARIgARIodQJUSEv9DbF9JEACiQT++Mc/qud/+Zd/mZiOD0mABEiABEiABEiABEqPABXS0nsnbBEJkAAJkAAJkAAJkAAJkAAJlAWBrxw9faksOspOkgAJlAeBwTfrGKUXP0sPJ1MeRNhLEiCBvkKA8q2vvEn2gwRIwCbwZ/YFz0mABEiABEiABEiABEiABEiABEigpwhQIe0p0qyHBEiABEiABEiABEiABEiABEjAR4AKqQ8HL0iABEiABEiABEiABEiABEiABHqKABXSniLNekiABEiABEiABEiABEiABEiABHwEqJD6cPCCBEiABEiABEiABEiABEiABEigpwhQIe0p0qyHBEiABEiABEiABEiABEiABEjAR4AKqQ8HL0iABEiABEiABEiABEiABEiABHqKABXSniLNekiABEiABEiABEiABEiABEiABHwEqJD6cPCCBEiABEiABEiABEiABEiABEigpwhQIe0p0kWtpwk/HTUY49c2FaYVB1Zj/KjBWNrQHlleZ8M9GD/qHrzZEfm4cDelHYvq0Vm4EqNLcvr70wPRj3mXBEigGwkcWB0ra6TWzobV3S9rOtq7X850I0IWTQIlSSDpd3VgNUrnb66MoVbjYElC7MWNomzvxS+v8E2nQlp4pqVb4vb781MUO+qx1FYwh1ZjHIBjz9XqPxgBha3jXAuAFqyfFa+0RkNqx5uLBitlVxTe1E/dFuCTpzG3y38ovHrjlGxp79t1g0voD2Q0Qd4lgV5PoKMJB+3JrKHVwHO1nlIaGKh2nNvS/bJmVm2eckbLliS5Yr+vg2u13AsNxIMyGO14c23UZJzUFz1w1mX3wESh3SGek0AigQ/xZORkeTvefG2L+ptbsIn0pHao31fS3/dpeGp3NeoTJuKTiveeye8zy29Qy42QHPAKyu/MGaslLSjkV3DGXJTt4TEkZbv7n+cr7hlPej0BGXCs3C7dqMHq3e/jgaFOlzrOotU5HffERu9+1h4PnYlZE55Wg77dTzSjYaqTccIzWDYFOLh2i7rxdt09qNq9Cm2qDcA4Sbu0MmstvnT31V/EU1N8t0IXpr/jnliByaGn1g35wc96GsesW+HTGty3EHhblOzKQN1GAccSfDulTeFyeYcESCAnAkOHoWPRYKwc+RaOrJvmZF2COkeWHNy7BW9vP4kqJeOasG97/rImi5yRBoisqR+eIGdSZMw4rMT452SiLvmQ9hxZBxxsqEfnlDpUmORD69CwG1g6azDajGxslcm4p00K3/e4hhWYHCF785L/vpJ5QQKFJrAEG93fuVP2gVex/pPAOKbQ1Vrlde5/D8ewBCMhVmRG5lgJ5HRoHZ594j3MPXceQNK4RpTJWqz/JJDfvpy1Gm0LRY7ZNyPOX6vHMlsOBJMcaMLBKdNixj+yqns/3g7mmfAMdm6rAxrqcRB1MXmDmQp0TdlO2Z7wX4kKaQKc3vZo8rqLODJ9NcbX6RUDd+BidWRkZZIgtRL6TivxwAvPYLcodaKw4RnvaUc96kWoOkKuQgZm6mkNZk3Npy5dtKxGhgSpV2tuZzKYO1Wn8og58VwZGC7Ug11Pqd2Ip5ZW4ql1kswS5JLuwdyqY2oSIIGuEDDy5n4sHW5NgEmRIm9an8HOU46ypibb8pc1uciZcU8k9MmSMV4qGZiuBF6wJge9h46cacS3T60PDQonL9Xyypd8aB3qFj6NlXub8NSUYepRWKEW2dWIb0coo76yeEECJUtAr47KhLY7qW61Vf3NFhmwzZqwsZ7nftqOA40tuK/+fd8kuBkbhMu7H+N9iuQSbPT9hivxwLaLeMDJqMqBPblmSlzvjDfMtf0tv+OXUfVCWh8bsXLU/XZG61zadRFPWXd8p0vTyvalLtAFZTtle/x/JSqk8Wx655MpK7B6whY1O/d2aHatBlVm1TTX3jmrpMc+qUFVJZwV1zN440dbcMxR7qRIPdMIYOGqyD8mWasND7TCOeP/YITT5nvnvukyW3o23+zMRwIkkA8BR960+SbQ2vHm68Cz1kBUyZuFq9CQp1zLImek+SJr6jP1owk/XXQWy9QKxEq0Pfw+noprW8dZIM26I1CnmnRU96L37/uTm5UaPWD2P+MVCfQ8gc6OdlQMTZ6o7mxYifUj38JOsSoYFWNVMOEMOjqACvPbci0Ugsqh6aOZZI5YdZXVWLHGiLB+SrXyEhPY12SiTFZQTV32dzs6WoH7Ho5ZdTXtNhP6JquaaBuNb0eWaRKZ77g+m+cl9k3ZHvlCKNsB7iGN/K/Rm29W4oGHl+gOjByhTb46zjjmqqMxNJOAi+q/nvU7csqe7T+J1k+WYPXwlx0nR01qplFya0XOKsfdu5Bl/4Ter5m2h1SbJ1t1dOn0vLN/Vdo3DFUTulQYM5MACXSJgJY3QbP9KQ+KqZk4TZN9ks7Khpo0ClSm5E26rJEV0jQ5I8+zy5ppeOoFqH1xT2Kjb8VFtVDaZZzLdQBVPisSvW/Mfe50SQbxyYcMtuP3699XH16BTS6PT0mgewhUdLzq/HZjyu+ox5PPAeNaG9Gx9H0cOXXR+jRjtfxdVsrbeky2xzLKekDK3IL6CGeLnQ0va4ur0ES5rMbCMhmW31K63PC3vjp+XNXxIXZ/krbVZwk2WpNsqmwZs02ojtZx/ZX3wivKdvPSKNsNCf3NFVI/j75xNWU9jpxa7/als/2kex57EjdTJwMocR4UdXzSgmNowbHngPvq3wLq7oepNdIUbuEzWN36NNb/qB5TggI4UH6WlQu9Qppirmf6FSgf4uDJMrs59tz9jtLegt37z2NWMD2vSYAEup9AgrxZOeokxk1oAWY348ip8/jpqFq8jRqMe20wxgctXMVq49QK7fhnXbxpWhY5I50+uPYedPiUxwQUZq9Z1H50yWZkj5KHgzE+sNfsvpFQ3jzdffH7V2J84zzLRLEdbzZ8GGhADUYmLzwF0vOSBIpAYMp67HziHuUgzG/mKm1px5s/OoO6U+9jsppQWu0zhT24VvZlympg9O958oPPYNx22Va0Em9OtSfOm/CG2r8d3qsqq7FixWDMa43iel9He2GUQVEsF84OmeS75GMUTz1mq3aTdcfJwbWrgXU9OFlF2Q7K9uT/yVRIk/n0gqfGLCtLU7dg5agY5dJkF6+1a0d4zkQCym2UmawZ1HU+UYO3RfD7zE+MqYysmtbhgXV1rvA3VXrf/r0X3v3oM2XioPZ8ArCEnWmPyhXY2xW3h9SXR/aQPhddJ++SAAl0I4EoedNag3HO5NfIh43DsUose6IGrefmoe7B963VEi1v4KyairyJPrSsiX4Wvjt53fveoNKRNX6Z4c9TsXQV7nvuZXeLRGdDPTqWGgcitomdLQ+1LG+b7ndSUrH0fWw8NxhzF8FRSs9jaOVMdMDv0KhKmUKKwxUeJFC6BCqmzsO4555GvTjeMg4S1aSP3m+tJmKmzMZ9uB/7DqzHZOU4UawU7N9NRP/ciaAWrH+9CQ84jpIOrtWOfULODw+sxtxzq3BEO71Qe7rfeA6uQ8iDe3UkgfFpY4EJlk+NQLPEAdt90800PXDwQBMmT4kx3w3khbFwC94PtIDGqQAAIABJREFUXWcY14XyODdaq62JrrhEBbpP2Q7K9uT/S1RIk/n04FNPcbMrNXsYXEXKeegNhtKUOE9hNWXZ5bvnrkIXnkU0aaQNK7fXYHX9POyuexrHJizBfdCu2VHfjKrGFoxbuATYfkZtqVAeIl0Pv1H7V6P7bOrL+XvhW9iI+7GybnWkoxApT4ekAcYN105Bcq6DGUiABHqEgJI34gzkhbNYOms0Np5aobzvjq+TgekKdDSORt0LUHvJXPM94+TINudTrS2grFGrr+shKwwHp8gKgydjQ2BmDXatRvDce0p2jsMZN5mx8vB5RXefeieTp1ty9cBZdAyl/PLo8KxXEYjcQ9gETLdXNafhqVMX9aqpeKyF5cgsobN6Iuh+vL39Zbz54DQ8AM/p4rM+R1/acRI+2eKzlJIxkt7zrfd+Jo6ZEtqhH2kP4G9vDzhotH1uiPXayNmeN22nTBmnZB+jpCjqSe10wrC4e3GT0hbwGWW7B5Oy3WNBhdRjUeQzEcBvAZabblsYyiz5kak6fMlI4/Y/S4vVHgadMMnDrmvWG7NvwV0ZXfgWHhh6FrtVkdVYtq0ZVYtq0bZX3LQvwcYXqlG//T1vkJi4f1X/0XG9wDnmtcgaLsaY41oCHtBhE6LR6D8QEhYnmwfgKCU6umTeJQESKBwBJW+MZ0px/qMOmXxrBha9Chx4Fbtnr0ADPsS+9nZgimOvquTNaNSFFNKwrPnp/pnKs3amVouseX0EGqzwFJPXmZWP8KSgmkCU1RdJL3l/5Dhj6qh3ZKdV68J5iPCnYiUAoFYX9K2D0t0pwAF/Cl6RQC8hYFknuPGGp6mVUF8HnL/vMt5RDoec6+TxwTQ8Vb8Eb0ukgR/VAyN1uLf7Hg6a+QZ+szIhv3d2KExd0pjJa2s7OjsqPQdLzgPX/Ncdr8nE1UrgQf8KaVjx1MrwyOk9YIM/1G+N4fWp+84o2wNsKdtdIFRIXRSlcOIJU2nNsedexcGllo2/2o/wFhpSRy9WX1yFUDsKgiscrTSJp97sv7sqawaIyqREDxJF0Mos/+ShTdiHFrQFvc7FKLpRVUtomVQzGTvj9vvx0+nGlM9+4D83fyDSPQAHB6/+cnhFAiTQXQS0vGl72BmEmmpc+SHyZgXebDjvDB5HAMG4gG5akzn6uzVXOQNkkjPB2g6+fgZ129aHVkG8dCNCg1nvWfCsCfvOjYgP5RBMzmsS6HUEHGsG2fpz6mLgd5Nhr7QbaeBpHQt04Vth52I+Jk34aR2w8ZRfUZQkra/dg/F1MZ5+fWWIHw17DCIO14Bxic4RtVM2zA4U5LsUFi87MZd9D3rhBWV78kujbKeX3eT/IT3/VAlTU+0W7HOnwcXE5CRWB2bXTMrob8csRR6KOetCz3vtT91ydU5jyurft2CUUXGVboStOB7Qs45enefRNtKEedEeat/eKwGmAdlDoY4s+yGGjsBIlVjX53rY2/0Mxqn7Yppied2rd7wJT3gGy1KV9HjHBrqB/n9lFk9531zb5IWy8SfhFQmQQEEJiLyRfWRG1kjhUfIGmDLVDB6Hoaq1UTkBktRK3mSWNQE5I7JFyZqAnJH7ImsyyZkgEDFFtCYVg49zvT7QiNbI7QZd8aCeayOYngQKQSDw25ZVylHyd1di817EkRjHh3qvdDsOHojzPm1FGojy+B9o+sG1ovAFf6Pn0fZJDUbKgESZ6F9U4yexWpNxiYylzLmSDQGlVztL2pjsHLHjPNpQg5HnJLyN7e1by8Bv75V7sv9V74kNNDviUuRnuodgsd4Yv6genREldN8tyvZUtpTt4App6v+Snk6ghel6x7OtG0tUTG8xD8+GTNES2uea69YoRXby0Is48qA2+9VecM3eA20iEi4pYNYiCUyZE2owrvUsOjEMBxaJ0HwGyzANFajEUBHi2xtxcN0wFYNLsoXNUsK1eXdasN7ee+U+yHfzvghDaaMMQIN/eHThb9fd45uFVA6TpouH4fsx16k/5BTBbRdPSIAEuk5A5M37/mIceXPfEzOd++14c+2HmOI6KxJ5IxN34vzE2fc1O+v+yjg5g2jnbwnOS/yNtq8iTBHtxznKxoN7T2LWg2LKZw3GnX363w6Uy0sSKF0CjoLy8DMYV3fG55RQQrckOV88Zo0N7G1NXl9lxdNz3uiOobwE7plMPO8T6ypnXKXMScV50u5qtGI0Zg0/CZxzk2c8acIBJ+SThJSJPYZOw1Pb9MTaU8Y5o514nRmvnUGdtVXATqK2Wk2YZ3kEjpdpdj6gBW8cqEtZOfbn6NoVZXsaP8p2UCFN+09SlOfKw9wWHTfrk6eV4FjW/h4we2PAfCWpddbsox17S7zO7gaWzpJVThH8Yqqywi0oqDiqvVDKZbqbRJ84Xi/njjKeHj3vvHqT9hbsa6hGqwppkHXPpqlDFEfLwYGzd+QYjALtpHMdMZl8Ud/yh0/cxQfKdJIqxdNR0pUSbHsIVrb9s/uQyUwUH94jgdIhECdvxIz/bcvb5Xp4nsBF3qzc24SnZG/7JzWY9ULWvVcRMkHJGgk9EZi4ElmTNLjMitDaQiFKZUcrgOEZM3eIg5bRqAsOXqXMjGbKGWtiMhLoRgKWGSqcPdUBD6yRlavf5nuYZY0NOg80oROVvnGR2ZojymrduVqs3P40nmyYGdofKp75VXxhy+mQmN0ekd+XPJtQjTpkCJkXauw0PKA891qTRqE0STcCe1LTftsBi5BoJd2rT8nYxnkZrMq8PIU4o2xPoEjZruBwhTTh/0jxHk1TIQ3edhTBt/euBmQgcirrQEsEqjgZkh7o1VFfX0QprT9jxRcV8xRfCvdCOVOy3KL/VJmQeAM5PaPomLWYWTyjUD/nKKsT5mFKlpVd1yOvW33XT9QKi9fe5AJrsPoF2/mBWVldgo1Z2p9cOJ+SAAmkEAjLG//+KSVvjLMjU5bIm7pGvDn8JI5NyGhF4siaKlNGUb613B35sF+ut9pOmiQEleXoTswH3fikTpvVKonlqfPb9QFluih9Y6UkEEHAckykViVdp0ZWWjX5ky0cSUUwhEpHPZ50Yo7WLa3E5A4TmzTgj0OqS1CC5Tc1bvYKDMV7Xtxg1UTbv4V1vjBxI6jVObEy01Zqx/x3Q1dqTyrO4Bji45Hm5o03VEXsDXdcp6zKrMWB2BzpDyjbAcr25P8nVEiT+RTtqeu+XFqwfQvelj0KmVvjmayMe2IjHohSpmxhLLMzTtlJXuW8mUenTDWrIxmDSu80fFv2q27XhY6bPdM3g5nejTizk2hTnsTyrTiknpCNa0FSvbKSzIFeHDneJ4FCExB50/rERtecTgZy9RJ2arfZP2pqFHlzP1Y+J/u6crEiifu9R5vsysqDCmVlqs3326x4RIWoGfkM/OEpjIM1rZh6+0f1dgq1x/a1Ftz3sGESNhH2D4LybTTzkUAXCRhFTPZk+kKwBMqdsh4b99pxdwPPEy4Pvu541TWTMm5s0i1YuXa2F189oQwcqMeTjS1QE0WyyOl48ZfxQ/3wZrXSap+rGOh7kwoMPLPGJIEnvkuJWRqcbPIlgEQNqMGs3f4JLX+afK7sLVwRDirzKTKQh7JdgFC2B/5b0GQ3CKR0rm2lLqjwJbXSrOppQdqQJPhDxSSFOWnHgXPzsPEJYGXAO2VY6bUFGpCk5PqaECeozR+yoMmuZFZmu7VYCv2Hwlde4EKZ5wbN3SSNW37cSqo2MeroANx4h4GyeUkCJFBIAu3oqNyIZ9vF4Yfn5VKUwvAEmydvCiNrIkx2pWsHVmNpw4qw6V8u3ZaJQMcBW+f+9/SKbsdqjJ+1RXvpdPfGBgvVzuJQGRh8KguQJaiborcm7J5ty8EIHwDBYnlNAj1FQP19n6lCpKRVqbf9vIcDHXURv/eY3MYEN+B8zJ3cN7FJfRP0AQsEKVr8Y4hPDPmd5mt1azVR++swN2qw2pymfE+eMg0H994f73/jQCPenjAPO339SSk002ORG2+hzbGGqyp4+ZTt/tdA2W54cIXUkCjB78kPOuYmWU1e7QDtvticyZ1TA6PkJAAq8YAaLNXhyFK/ENdhWjxl7uBavWfzPrVK2gIRyGnhZtJXL6WB0Suk8kTakEUpTe1mZAKZyTIrEJEJeJMESKCgBCp1XMIp7/vkjQkJZYdYUPIGz2DjE+9hZYFkTbxTlS2FkTPKtBBYvbtOhXw5cmoF3lw0GOPrAvvkXabR2ypkRUjiNIoZ72QVo7UW48+9lW0lyC2bJyTQUwTC8Tojax5ajXELZ2dXRmXs85p2ZBSOOeptgZLYpFOU917jW8JbATXtkLHIvunvK2uIQnii9WSVrjMUEs9UHPpO3mMuHsXvezgYFidUSJ43nBCEr1Vn226VUy2U7X5clO2GBxVSQ6IUv4fWoW7h06gfnsXk1ROwaZva/V014VBkZjB9r6e3Md1TQM299a/XA60m9tcqPLVuGpYNvwdzn9NKqZgdq0Dx/gaoq9jVS3nqrmDGDdYiCizoLYctnsHOGFf0Ba2OhZEACSgCWraI4nYRR5yZenXvtXosmzITB8Rh2ci3cER5q6zDTtyDuXWD1RaH/GVNzAppod6JI89EkfRWe2VVohlYVKs8jHqDWKdSWQ2REBZWG4TDStlP64a8qsQDLzyD3bOyxWW2iuIpCZQWAVlNjbJmimul8ZkRCL9iklcs3YjVjTJRrp1EPjUlxnpAtgW0PoNnU+teAtmjmtuh64zNo+SC5bTJ8X8R6aRNbbOKjklvJu1i65EHE+YlPobsXVexWW2fGilZcnxM2e4Ao2x3/+dQIXVRlOJJ1j0CRhn1lMTo3ph00U/DM4tWOsujbVDhVZvVp+qN+usDm+DlmRokiqOB7fdjvNpXWnjF0lthDTBwlVmrL7Gn8XvKdJanIV6FQ4PF2PL4gARIIC8CjrwRWXMk4MzNlTejVmKWpahKPZ686W5ZE5AzUnlA1ox7Ihx+Rg/CRqt4ykHnRMoKxZjKiddg22GLrBjBc4oh5TyJjTiyLjAoHjoTsyY8jd3KMRIcD+PeGxj3hHfOMxIobQJR45Wa6Nietk+MyE7FKKC+tBKZ4Azqtq2P3Csuk+bmNzv5wWosHTUYK03+OKdGs9/KHlrFMWlWlhLGyeSEZyJC/Zl21pnanW+90hYcnwUSQcmgxuBd+1os4CQWbDf5zKBsp2y3/7tZ5zccOXXxunXN01IiID/cvckb8WMVsdR+2MI+YnCl8ttpwqYtyqGGCqniVGaHTAnWr4TQSX84F0ALx6iwMoH899W/BdRJLNH44776ZlS95qyYGI+/8cn5pI8SGHxzP9Wzi59d66M97KvdsuRNpDWF9TxJ1gieA/V4c6h//5lWBr39qJEUVbkj8Ibt2TaQUE9ItePNhvN4YGmEKb8opq+PQIORQZaimjZYVFUdWI2fYn3MQFZvl2h9wt4rGmhgxKX6O9FKC48INL3uVp+Sb+q3Ya0Kxr0NR4kxDobikuV/X2TLSuAFv0dZM/ET64vDaVfBJ6ljx37R7dT9bsLBA2GnZvkzKWROS3ZTtlO2x/zXokIaA6Yot83AxRlsdZigza5JVlFaxUpJoFcR6FMDtl5Fno0lARLobgKUb91NmOWTAAkUg8CfFaNS1hlNwHUu9Il4l2vCvlbH01t0ct4lARIgARIgARIgARIgARIggV5NgAppCb0+5Z5ctUf2Mjbi23SgU0Jvh00hARIgARIgARIgARIgARIoNAGa7BaaKMsjARIoKgGatBUVPysnARLoRgKUb90Il0WTAAkUjQBXSIuGnhWTAAmQAAmQAAmQAAmQAAmQQHkToEJa3u+fvScBEiABEiABEiABEiABEiCBohGgQlo09KyYBEiABEiABEiABEiABEiABMqbABXS8n7/7D0JkAAJkAAJkAAJkAAJkAAJFI0AFdKioWfFJEACJEACJEACJEACJEACJFDeBKiQlvf7Z+9JgARIgARIgARIgARIgARIoGgEqJAWDT0rJgESIAESIAESIAESIAESIIHyJkCFtLzfP3tPAiRAAiRAAiRAAiRAAiRAAkUjQIW0aOhZMQmQAAmQAAmQAAmQAAmQAAmUNwEqpOX9/tl7EiABEiABEiABEiABEiABEigaASqkRUPPikmABEiABEiABEiABEiABEigvAlQIS3v98/ekwAJkAAJkAAJkAAJkAAJkEDRCFAhLRp6VkwCJEACJEACJEACJEACJEAC5U2ACml5v3/2ngRIgARIgARIgARIgARIgASKRoAKadHQs2ISIAESIAESIAESIAESIAESKG8CVEjL+/2z9yRAAiRAAiRAAiRAAiRAAiRQNAJUSIuGnhWTAAmQAAmQAAmQAAmQAAmQQHkToEJa3u+fvScBEiABEiABEiABEiABEiCBohGgQlo09KyYBEiABEiABEiABEiABEiABMqbABXS8n7/7D0JkAAJkAAJkAAJkAAJkAAJFI0AFdKioWfFJEACJEACJEACJEACJEACJFDeBKiQlvf7Z+9JgARIgARIgARIgARIgARIoGgEqJAWDT0rJgESIAESIAESIAESIAESIIHyJkCFtLzfP3tPAiRAAiRAAiRAAiRAAiRAAkUjQIW0aOhZMQmQAAmQAAmQAAmQAAmQAAmUNwEqpOX9/tl7EiABEiABEiABEiABEiABEigaASqkRUPPikmABEiABEiABEiABEiABEigvAlQIS3v98/ekwAJkAAJkAAJkAAJkAAJkEDRCFAhLRp6VkwCJEACJEACJEACJEACJEAC5U2ACml5v3/2ngRIgARIgARIgARIgARIgASKRoAKadHQs2ISIAESIAESIAESIAESIAESKG8CVEjL+/2z9yRAAiRAAiRAAiRAAiRAAiRQNAJUSIuGnhWTAAmQAAmQAAmQAAmQAAmQQHkToEJa3u+fvScBEiABEiABEiABEiABEiCBohGgQlo09KyYBEiABEiABEiABEiABEiABMqbABXS8n7/7D0JkAAJkAAJkAAJkAAJkAAJFI0AFdKioWfFJEACJEACJEACJEACJEACJFDeBKiQlvf7Z+9JgARIgARIgARIgARIgARIoGgEqJAWDT0rJgESIAESIAESIAESIAESIIHyJkCFtLzfP3tPAiRAAiRAAiRAAiRAAiRAAkUjQIW0aOhZMQmQAAmQAAmQAAmQAAmQAAmUNwEqpOX9/tl7EiABEiABEiABEiABEiABEigaASqkRUPPikmABEiABEiABEiABEiABEigvAlQIS3v98/ekwAJkAAJkAAJkAAJkAAJkEDRCFAhLRp6VkwCJEACJEACJEACJEACJEAC5U2ACml5v3/2ngRIgARIgARIgARIgARIgASKRuCG69evXy9a7ayYBEiABEiABEiABEiABEiABEigbAl85fLnX5Rt59lxEiCBvkfgpq/+herUF//93/te59gjEiCBsiZA+VbWr5+dJ4E+S4Amu3321bJjJEACJEACJEACJEACJEACJFDaBKiQlvb7YetIgARIgARIgARIgARIgARIoM8SoELaZ18tO0YCJEACJEACJEACJEACJEACpU2ACmlpvx+2jgRIgARIgARIgARIgARIgAT6LAEqpH321bJjJEACJEACJEACJEACJEACJFDaBKiQlvb7YetIgARIgARIgARIgARIgARIoM8SoELaZ18tO0YCJEACJEACJEACJEACJEACpU2ACmlpvx+2jgRIgARIgARIgARIgARIgAT6LAEqpH321bJjJEACJEACJEACJEACJEACJFDaBKiQlvb7YetIgARIgARIgARIgARIgARIoM8SoELaZ1+t3bEP8djAfhi06kP7Zjec63oe21Poonuq/THt3vMIBg3sh8L3K6Y+3iYBEshA4EM0dYes6XY5maFrTEIC5UCg7Tza4vq555ES+psrY5BH0BTXVt4vMAHK9gID7RXFUSHtFa+pQI18414MGjgN9bF/AXKp5zzq/7pfQFGbibuXAVsW9sOcn5/PpTAnrSkzpo3B9juK4qAu/6Ew9Sa3W/pFpTSP18osJNBlAvo36v/9zQQ+SP7Nxlcr5WWUM1KIkjX5DEh1u7PKw6ZVQZnq9KDtVczxye7zqF/1asRgXuqLbqcuO6bP8aD4hAS6kUAjVkZOlp9H/Uuvq7FE90+kA1C/r6S/7zPxUstt+NuB+cobgzBB7pgk6lvLDb+88yXI78IdM3W1H/lVH50rqq+U7eFhet+X7VRIo38hvfKuGcyElM62Mzjp9Kjm2c2oqypA99oa8e5hXc6Whd4gp2rkRHVz9IhheVQyDHW/OIaf3HkIP66RgVl4YLVke5PTfv0HS1fyOhYlrWo4f2xklTP6Mw4/PjwRS5ZNRMuT48JK5/DbUKMqehB3z8ijW8xCAiTQNQKOvLFljRQo8iZ/WbMZeDhNzkgtRtYkyJlYGSOyBcA/PRQje/wy6YO7r+HSlWu4+2xA2axagV0tC/BujTVo/t3jqA3JNKnvdfxtzIRgweR/194mc5OAReBBbHt5pnUtE0B/r/4m/6TlGi4Fn/lTFuSqbfcOtOBBAAlWZFUrsPHZiWhpPZtSp1aw4scaMr55BI85k0/x6YAtLwXkQLDmPR8mrNo6lmW2jFj4OnDni2i+cg0b0ZiQN1hRN15TtlO2O/+9qJB24++sp4ue9vI1XNouQlUrdFGza/kN3sI9afrZ42hRtx/EtitGSQSqZi1QytuWDxIEe7g4684w1L32oqMAxg8A237+kB7oLXtHDeAS/2jJYO6KHug1P6sVZjj5ti3TVctA7aWXm1RZL82wBHmSomu1mqckQALdR0DLG7+skdpE3pzssqyJlzNSh5I1t6fIGUvGiEKpPzK5NhEyqN71Cy1bvGeS5h0sgfTJpL+Gl5wJr2nfW4HQvGHVCvxQLFCs/i7Z7uXVZesyf/i9fCYEu+/9sWQSyE5ATwDFTZ6oife/TlHUslemJpz++Z8OYcn2DXhphqcYexP83qRR7ZOHAGWp5d0LT5zLxLr3u1RjDDNOcX/rG/CSjNfc6+C5/I4n4ievRcgBX9/exyJb4fSdv4+7o8r/hS6z6nsrMM1XVnEuKNvlDxllu/zvo0JanN9g99U64wf4yZ26+PDs2kSMHF6Aqttexd++ocsRIe4TalWzMV/qf+OF/E2DnZlIqWHJ3d4fCLflba9ipfxhECHvzp5qJTJKCXfz5XESWX8e5TALCZBAngQceROSNVKcyJvfdV3WxP7ORda0/siSM1Jp0v6mD/GYM1gWRbb1MW+yLtT7tjPAsz/wy89QIv8NNenoyjz/s/CVWakJW5qE0/IOCXQ/gba29K08ZgJoI6KtChapscdptNk2ja6FQtz/dTPJ7Flzub2V1djb33Eng9z7AGqePZagNDoLADLesdtiF4DzOPu7mHGMpDPtDirYyqptLEaEZqV8hTsX/kktT8kNjM2ishb7HmW7+wYo26mQuv8Z+s7JMNQ9JqukAG6v1rPs5047q5lZBVwyDXd1dFmUEDf1H8KPH85/FrPqe2a1MtiWD/FYzeNoEbMTd2Amf2zuxRbo/av5KaVnnT2x8gdrBEY6Sn2wdl6TAAn0LAElbyJljbRD5M3YLssaszLp79mHeOxhYKMrZ+SpljWLYveTz8RLr0Hti1uJzeFBruzhMlYX54CRs+yVTEeBNM+dxqQP4vVgO26faqQi7+8or0igRwhUnfv7yK04buVqshmo+d37aHPGAJ6CJRYH0Canv9iAabay5qwwAdHm6m0/f0GND7DsR4EtS7IaC8tkWH5LEUqr28Cok9tQZbfFTqLMUdO2+jyIbc6qpZtVxmx33ha2knAT9I0Tyva0CZryku1cIe0bv2t/L2Zs0LN6zkCq7exx//Ooq7iZumDaPY9Az1BG7PswaWdsgDJTOfw4agODK5PE/bY22UfvpRDTGK1sSp4tC+8Fnn0RNVK2a54iz/UsodRrm7S5M5BOWmVyIwU5Zje6L0DLk/dqE2Acwru70/aIuK3nCQmQQHcSUPImQdZI3SJvbi+srBEHZoMGvo+7HeXSk02eGdzdcfvNzF6zqP3o0l5j8vcBnD2sxvxP7zddAvj3du1+CIN8KyjnUf/zxgB12UsbuMVLEig1AjM2oPnZ41gU4R9C7dV++DR+eKUJux5DKE3TKvl9RChvTh+nfV9v9Wl58qGAddaHeFUsqmSM4Jtc0ub4rY95K4lGcW09l6YoZAQriuWye+KtIGIUz0xjtoxNiEvWtCpuNTkuR4HvU7YDlO2+/1RUSH04euOFMcsyg5rwt6uE4fX4/Qay6ijdT1QiP8RjsileTGmDproBdNNelj0QzuArSSk1ynPEXgezv3PJdqcsaMdDW57UbfWb0+g/KiGzh8Derrg9pPZerF3fA1odh02BbvGSBEigxwhoeZMma6Q5St6IotcVWaOcfWhZI/JADZxrHgd8Znve4HWatd8siKTqez9Se8DMFom2n79qKZmOid3LK3x7zS5dcVaA7p7pG8CKtYhSuF2l9CxGjJgdrBIjh9urraHHvEECJUFA+5kIr2Q2rXoIeM35fc24B0vwOj5wwjrJfs5Fb8jvxvv9hTrjbvU5hB//zPNh0bRKT2jXBM3j9zyC2tYfWVYMorgCP2lpwksz9G9JnBx6k1HhsdUgZzwUaotzo+mD133bjpr2eO2Ky+PeNxZu7o24k4RxnTtpH277ojdexyJXpsSV3V33KduFLGW7//8XFVI/jyJe6aX5oPAzZlhtP5/mE4yeWap/A71n3mI2yTuDnLT9EMoZknQ/PIuooYjiawT7MUuIxyETN+mOcyK1IpCbGYz+A6TNc1ZYnm1Hfl+b8opiaf+xMJziWmPut7XKTClQM5LLCYYJv0mg9AhoeXPy2SyyRlrvyJt8Zc3vXkRzwGzObBuw97JFy5moSUGRlcZbeD/UPvk4FokpIIzHbk1cO05Jl40/EPsMAAASRklEQVTT7n4QOHxab1XbcwZnC+ELoPReOltUDgQcPxN+B4sfAnfb+61n4qUr4uRL/7YWye8zSRl1uOmJIMuHhfF3ceeL2Ohz9OV4zjbWCkpxuxcn3SgEeu+nf9LbjKkC3wG54b3CD/HBG3obkRnXLVronzRTK6ERiqeMU7KPUeL2kAbaGTHpf+m1av9eXK/x3XhG2W7DpWz3aFAh9VgU+UwEsFkJ1E0RYbjLEaJqcOQoeDJ7H73nKaILVngW/x8Af1rXRCRm34I2l5GV0Wtum/wl2FfOAE1WF5Y96HjM9QZniasYEmLhr2U2FNppUYywN4NFs4qqldM0ExT9BwKYiPm+vVt22+3zAjmBsovkOQmQQCoBkTetj2WRNVKUI29kv6eSkTnKGryDSzFyRkq3ZU20nAlPCipLDONZU9qkVl+bUBelSC5bgP+aRkRZkujVoaazwH+N27OWVg6fk0DRCejfi38MMxPTrIln1US1jUjLAfX7dLYVRU8KmU7NxEsm0sDDr6LeiQaw5LGgt9rAb1byLHsnNLZJGjOZGkX++BwsOQ+M+a9nfeV43f6+31FjWPHUynC2ur1W5HVWNdO/FzevQnLLRNke4EXZ7gKhQuqiKIUTI0x1W1qe/HvLzAuA2o8Q5Ugooe2Sx3ks+6K8ldWEPIFHZrVyyfZjGPlS2PTDzP55307sPQAtv7sNG69c03tKZfVVBP/vzkQ6pdOrwJJXh0rwPOg6DbpzQWgg5oW6kTSeiU+gC+rS/IEIOzYIptazs5escDbBFLwmARLoHgIibyQep1kd8eRKnOx5CO+qpsgKog7xJBNVanXj+yMSZI2YCEbFOYx2QuLJmmQ5E6TS9LPT+GGCwgtUxztFCRaGD/FBq+OsLvSMN0igLxBwrMVkgkmtktp9yrBX2kQaOPw4fuxMbPuVX7s8ORfzUduxkff85Et+y7RoWTQOtXZ8YJX9PP75n4CaROeIkkZbbHk1Bs/ycbIULKN0rinbk94FZTsV0qT/H8V4ZoSpqtse+IiJyXH8JDC7ltxExyxFEi17RymF2llHWDE1pqyuZ15VsLdaqVdlA7OK1opu0LTFrFzWfHe28hSnBnNiciOzQcHBmePYSPa66nJs0x1pg5i/JcTksvahxv/hiXdsEMVQm9L1U3vSdNDsqFS8RwIkUDgCWt5oZVRKjZY3QVkjE0c/vF28it/jzvaLvFHWJVXD/J4qHVkjHnDDE05a1iBpf7wja+LlTJCGmCIm7HsLJk+73vM+TkZuNyiMB/W06vmcBApH4DzqHzbxzAG4Dg4dx2HBcYJTsd4rfR5Ne+IcDxlP/zpDbEgnp7ymVS9gZEvwN3oWrYcnYrTIFStmuZE97oSXmME6q6u2TNAhnzZjfhKstrNoxUSMbg2Gt9ETZXd/IBNwjum/tSc2vkiRX+nm/2riv8f3jlK2x7835wllO+OQpv4n6fEEfmHqxhIV01uEVwgTm+ea605UiqxSCh2zX62YGhNXbSISLssbENrC1k2nYmXJVdAE1isvzexECUdxDOAIfWOi7NbhnmgzvPgVXi3wos159EBTKbWhPzy6gi0L/YLcXQ15414Yp1Ahpwhu23hCAiTQdQJa3kTKGilcyZugrJEHzn6vSEXNa5WSNR/cozyQx8uZLKGjkgbDXn36LMIUMZAkbLIXSGBdNn1wPLzdwJXDVkKekkBJE5C/yQ8Bjzl+JkQZdR0ExTjpUY4XjTn+OCxaOA7Rf+8954uCwB1DRfDQK3beBLieiH4ETU4c0JEjIzKl3voQ/xwV8imYr2omXvpFE156WfvF8Px/6PZ447U4vx6A2mrl22Zl+MRZk8h+9kPKeeWrjsOoYLO655qyPY0rZTvjkKb9HynOc+Vhzqn68OMQwSGrdHBWG7M1ypp9tGNviddZRykVE9eg+/VcBkfKhFg1Jv/ZebM/K2Se63ZSZir1RfzeWfnjps2EZY+XX2k1z8QM2PvDY4r3hL4jyO2ZQ7UaIvt6tQlx0iDWlMdvEiCBbiKgth90UdYEwj74W6plTbyckdRanshg2C9n/CXFXllbKIwiHZs2+EA5aInov5TpG5QGM/KaBEqJgJihyiqgtafasnLyFLOAUx41bnG28zgOejaOOBsyyTdbc2Q1U1lqHX4cK38esZrqhLAzVmNijivWGZfEksv5TeXn+nAm6nwOlHJlH9iTmvbbDjhFMqu4cRzV/vY7X4TtLDLXFhY8PWU7/vYNyvavFPw/FgssAIGZWPHsRGxRsbMkruYjwBtj8cMrObj13/P3TlxNvTrqa5QopdtPWzOSntLnS5d44ZkD579yKH+YtOfexKqch/KHY0tsQq1wjvhZPyx66VWsmOE4MVCrxNHKaLiooFmwDD6lfQ9iG52IhHHxDgn0GAEtb2SwNS2vOrPKmomokX3yC+MqcQbEVUDTz19Fm5EzcckT72u5O/oxv1w/efY84ISdUPvbbBm57J1Q/7Wnzntc0+S7k0yOE9vDhyTQzQTEMZETSukl+ZvaFlGfrJa+dFvI63VESlQFQy+1vYqVTszRH35vGKa1vYiaNx6H8sfxvYBZrlKCN0QVq1Yea777A1Rhh44bLHtR1TEOg56MOF92j7mZ/n1OMzC+PeIyqIkxiA+Q2+KSIDdvvLHFhB7ISrGO0Z517BQqIocblO0KFmU7TXZz+NX0aFLXfbnU+sbr2JIUXDnUMs9kpcZ1Yx5I5M5IbsA0y+QrzcTWLcUyBw57rDUKbpqXWuM8KDATarknd/eiSixAs7JrvFda6cx+MLXiae89UXFI9cqouy80GJvLxGC1wjRo5wXGOVN4JdnlwBMSIIHuJ+BMLIVljVTtKHYj/Iqdv1EZZc2yH2HXL65pWZMgZ6Tsad8Leu701xh7ZVY8HBNkE6tUpb89GJ7CtFt7YPcsWIwJnHaM4u2TC5sIKwU3tjF8QAI9RMBRRlsivNn6WjBjQyDuru9p4kWT8aprJmXc2KSvY1FSjGK71D2vYuU/HYI7Fsqyh9TOn3Y+XDtei1vBNPfvxofaJDewAuoVL1EDorYweCnyO/O2XAGH0Houv1Iy56JsxxJfKMLyle10apT5V9PTCWfi7mWmzohVTvMo9G1W9fRm/NzMTNMUSK8yI/gjPdZaCq6XI48zE0MMD0JmO6FWdh/UM5ZZ/7hY1Spl1afEOoqwUXQd01zzB8H7lsHgcZyNms21yucpCZBA9xBQ8sbeemBXUwh5o2SNJWdF1tz9vnJqZlfV5XOZCHQmzJSzNPEcfu4RFWP6sT3DUPdynJI7AiPvhDdINg1RgznxCixyv19gT50e2OT2N8AUzG8SKDABNTl8DM0ZHDPq2Iw78M+5/M11THARMEd1J/ffeAH1ofLEciKw3/KlHQAKZ9LqmQSbCe5sXKfNmJm8ArrnfWyJiDyQrfSkVCI3TAjC7GPCpBKTnlG2U7ab/x9USA2JEvye9n1nw39moaP3N/1Y9lzKrF7ifimvwzl7kbUEf3NiHRE28V61KWcf4jFn5XKJme2UHDM2QO2BUAGt/Y6IUgrswmNZpQjvP+1CgcxKAiSQlYDIm9+9iGRZ05WBk5Y1CFqTiKwZ+QIGDewGOaNMC4GfvLYCVcpaxYTUMo7mgnCM1Yn/vgzmRiv5KIPIY5j/T+MKr0T7q+QVCXSBwLBsIY6G34aauAmoyNq9LUThmKN6C5Ss9v344VcdK2E9gaO82AYsIbbdfgijQ3FLIyvNdFNMb/XktsQhzWXF0V6pDFfV9MHrCPc1nC6/O04Iwsxjz/xqEc/KlO2U7eZ/DxVSQ6IUv6tW4IcSTy+TMyNPGVWb2hMVRbuzJhwKgCzCx3jDk0DvtmmsVaS3enpPaL+TlSzh1NvvJX0Jet1UjpBM8Osamd3shgGjr3XOHy/b4ZHvOS9IgAS6hUCG/WRK3mSRXZENdGRNjBmhljVj8eNCyhnHdHH0dnuSSyuUP7lTexgNOUyS1ZBA+8Vr8CLYcamHoe412TN3b34OlwLl85IEikZAVlMzj2EkZIzjM2OZ/XvwWl/1vc34icQEdZxE+kJK2fWIpcTvsqyOOlZbXhUZzrTFQnA842YUuWCPZZJMWaWdvt++WwrEsWN0vFS9EmyiBng5os6c2KwxY7yoHDnfo2zXyCjb3f86VEhdFKV4knWPgFFGtcONeBMtMytom6h4ToXSZtt8IVpsQaUEqVem3gwPePuasrI17ZM2pfRFrSoYs5Kgq3NrlSHQtkhBHbuH1PTJMbU5/DhqB4ZjuGbtHdORAAlkJ+CGabFljWQP/KZF3mSbtLPrNrJGYhBeS7YmcWVNgpwJtEvFVI4IQ6P6VHMaP7xyLTTRpgfJWqZt+eBDu7GArBgBMPtBpRwVS9UeTEuOqtmYf6dJZ/qo5ZiRy/6CeUUCpUrA//93kPt3OqK96jea9DvWyqCsVMYqhOJB++HT+GFQ3jjVyZYfM7aa9v3b8LfG1NcNVxPRru9GK8gRKfWWpCubgYedcYf0N3KizWln8Lfv7KXP5GU3sgHmpkzSSSzYgBMo87gA35TtFkTKdhfGDZeuXLvuXvGktAjIDJLEzQsJHq+Z+XtDE2Fv9jQke1Lz6gBE2Bmh7LXCnPlXNuPTmfR2G8y95LaYVL5vGaCqP1Z55PUVxIu+QOCmr/6F6sYX//3f+0J3yq4PRt4kyxrBouXNyUSZZPAFZU2eskLJmh2YHxFCytSkFOafVXsrPK58SpOfTgl7HsFj2BAzcM6lz26LoJiK2XPMYNtLybNSJ9Cn5FuW35O8EGOZlcNWpNzeo8gHJxSN5VHfTPzEjmWcdiWHisqtJSp17Ngvup26hg/RtCfs1CyP2rstC2U7ZXvSfy4qpEl0evqZGbg45rBtq3RcrPgZvZ5uIOsjgdIn0KcGbKWPmy0kARLoQQKUbz0Im1WRAAn0GAHGIe0x1OkVuc6FDot3uWq0yl6Gl9PzMQUJkAAJkAAJkAAJkAAJkAAJ9EYCXCEtqbfmmbwCD2JbN9rwl1S32RgSKCABriAUECaLIgESKCkClG8l9TrYGBIggQIRoEJaIJAshgRIoDQIcMBWGu+BrSABEig8Acq3wjNliSRAAsUnQC+7xX8HbAEJkAAJkAAJkAAJkAAJkAAJlCUBKqRl+drZaRIgARIgARIgARIgARIgARIoPgEqpMV/B2wBCZAACZAACZAACZAACZAACZQlASqkZfna2WkSIAESIAESIAESIAESIAESKD4BKqTFfwdsAQmQAAmQAAmQAAmQAAmQAAmUJQEqpGX52tlpEiABEiABEiABEiABEiABEig+ASqkxX8HbAEJkAAJkAAJkAAJkAAJkAAJlCUBKqRl+dr///brmAAAAICAYP/WYvzgGnAmShMgQIAAAQIECBAgQKAXcEj7DSQgQIAAAQIECBAgQIDApYBDejm70gQIECBAgAABAgQIEOgFHNJ+AwkIECBAgAABAgQIECBwKeCQXs6uNAECBAgQIECAAAECBHoBh7TfQAICBAgQIECAAAECBAhcCjikl7MrTYAAAQIECBAgQIAAgV7AIe03kIAAAQIECBAgQIAAAQKXAg7p5exKEyBAgAABAgQIECBAoBdwSPsNJCBAgAABAgQIECBAgMClgEN6ObvSBAgQIECAAAECBAgQ6AUc0n4DCQgQIECAAAECBAgQIHAp4JBezq40AQIECBAgQIAAAQIEegGHtN9AAgIECBAgQIAAAQIECFwKOKSXsytNgAABAgQIECBAgACBXsAh7TeQgAABAgQIECBAgAABApcCDunl7EoTIECAAAECBAgQIECgF3BI+w0kIECAAAECBAgQIECAwKWAQ3o5u9IECBAgQIAAAQIECBDoBRzSfgMJCBAgQIAAAQIECBAgcCngkF7OrjQBAgQIECBAgAABAgR6AYe030ACAgQIECBAgAABAgQIXAo4pJezK02AAAECBAgQIECAAIFewCHtN5CAAAECBAgQIECAAAEClwIO6eXsShMgQIAAAQIECBAgQKAXcEj7DSQgQIAAAQIECBAgQIDApYBDejm70gQIECBAgAABAgQIEOgFHNJ+AwkIECBAgAABAgQIECBwKeCQXs6uNAECBAgQIECAAAECBHoBh7TfQAICBAgQIECAAAECBAhcCjikl7MrTYAAAQIECBAgQIAAgV7AIe03kIAAAQIECBAgQIAAAQKXAg7p5exKEyBAgAABAgQIECBAoBdwSPsNJCBAgAABAgQIECBAgMClwADtdWUSYUKHaAAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 本章，我们主要研究“数量预测”，使用的数据为“cars”，使用的模型为“线性回归”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speed</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>24</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>24</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>24</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>24</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>25</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    speed  dist\n",
       "45     24    70\n",
       "46     24    92\n",
       "47     24    93\n",
       "48     24   120\n",
       "49     25    85"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = pd.read_csv('C:/Users/灬月光皆旧梦/Desktop/使用python进行数据挖掘/datasets/cars.csv')\n",
    "w.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speed</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   speed  dist\n",
       "0      4     2\n",
       "1      4    10\n",
       "2      7     4\n",
       "3      7    22\n",
       "4      8    16\n",
       "5      9    10\n",
       "6     10    18\n",
       "7     10    26\n",
       "8     10    34\n",
       "9     11    17"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['speed', 'dist'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   speed   50 non-null     int64\n",
      " 1   dist    50 non-null     int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 928.0 bytes\n"
     ]
    }
   ],
   "source": [
    "w.head()\n",
    "w.info()\n",
    "# w.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=w.iloc[:,0:1]   #X作为特征，必须是二维的 \n",
    "y=w.iloc[:,1]\n",
    "X.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#或者  也可以写成  X=w.iloc[:,0].values.reshape(1,-1)\n",
    "# reshape(1,-1)转化成1行\n",
    "# reshape(2,-1)转换成两行\n",
    "# reshape(-1,1)转换成1列\n",
    "# reshape(-1,2)转化成两列\n",
    "\n",
    "X1=w.iloc[:,0].values.reshape(1,-1)\n",
    "X1.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1=w.iloc[:,0].values\n",
    "X1.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LinearRegression()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1,)\n"
     ]
    }
   ],
   "source": [
    "x_new=np.array([40])\n",
    "print(x_new.shape) #模型预测输入的数据必须为二维的，现在的维度为一维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1, 1)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_new=x_new.reshape(-1,1) #添加一个列维度，现在的维度为1（1,1）  或者写为x_new=np.array([[30]])\n",
    "print(x_new.shape)\n",
    "x_new1=np.array([[40]])\n",
    "x_new1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[139.71725547]\n",
      "[3.93240876]\n",
      "-17.579094890510973\n"
     ]
    }
   ],
   "source": [
    "print(model.predict(x_new)) \n",
    "print(model.coef_) #模型的系数，为了和用户的变量名有所区别——sklearn的系数总是带一个下划线，\n",
    "print(model.intercept_) #模型的截距"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 下面是画出原数据的散点图，以及回归模型的预测直线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2562780d788>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "line = np.linspace(0, 30, 100).reshape(-1, 1)\n",
    "reg = LinearRegression().fit(X, y)\n",
    "plt.plot(line, reg.predict(line), label=\"linear regression\")\n",
    "\n",
    "plt.plot(X, y, 'o', c='k')\n",
    "plt.ylabel(\"Regression output\")\n",
    "plt.xlabel(\"Input feature\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用随机森林建模 f(x) = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regr = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_new=np.array([40])\n",
    "x_new=x_new.reshape(-1,1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([88.50222619])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.predict(x_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  一个最核心的问题由此产生——随机森林和线性回归都可以预测，那么那个模型好呢？我应该相信谁呢？所以，模型必须要评价，要对比。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ★关于随机森林(bagging算法见“第一章关于bagging算法”)\n",
    "\n",
    "- 随机森林是一种有监督学习算法，是以决策树为基学习器的集成学习（Ensemble Learning）算法。随机森林非常简单，易于实现，计算开销也很小，但是它在分类和回归上表现出非常惊人的性能，因此，随机森林被誉为“代表集成学习技术水平的方法”。\n",
    "\n",
    "### 随机森林的特点\n",
    "\n",
    "- It is unexcelled in accuracy among current algorithms/在当前所有算法中，具有极好的准确率；\n",
    "- It runs efficiently on large data bases/能够有效地运行在大数据集上；\n",
    "- It can handle thousands of input variables without variable deletion/能够处理具有高维特征的输入样本，而且不需要降维；\n",
    "- It gives estimates of what variables are important in the classification/能够评估各个特征在分类问题上的重要性；\n",
    "- It generates an internal unbiased estimate of the generalization error as the forest building progresses/在生成过程中，能够获取到内部生成误差的一种无偏估计；\n",
    "\n",
    "### 随机森林是基于bagging框架下的决策树模型，包含了很多树，每棵树给出分类结果，每棵树的生成规则如下：\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- （1）如果训练集大小为N，对于每棵树而言，随机且有放回地从训练中抽取N个训练样本，作为该树的训练集，重复K次，生成K组训练样本集。\n",
    "- （2）如果每个特征的样本维度为M，指定一个常数m<<M，随机地从M个特征中选取m个特征。\n",
    "- （3） 利用m个特征对每棵树尽最大程度的生长，并且没有剪枝过程。\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机森林的分类算法流程如下图：\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 吴恩达老师的《机器学习》中指出，如何优化当前的机器学习模型:首先要知道当前的模型是处于高方差状态还是高偏差状态\n",
    "-  (1)高方差需要增加训练数据或降低模型的复杂度 \n",
    "-  (2)高偏差则需要优化当前模型，如增加迭代次数或提高模型的复杂度等。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机森林是基于bagging思想的模型框架：\n",
    "\n",
    "- 从bagging角度去探讨随机森林的偏差与方差问题，结论是bagging法的模型偏差与子模型的偏差接近，方差较子模型的方差减小。所以，随机森林的主要作用是降低模型的复杂度，解决模型的过拟合问题。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机森林的相关性理解：\n",
    "\n",
    "- 随机森林的相关性包括子数据集间的相关性和子数据集间特征的相关性。相关性在这里可以理解成相似度，若子数据集间重复的样本或子数据集间重复的特征越多，则相关性越大。\n",
    "\n",
    "### 随机森林分类效果（错误率）与相关性的关系：\n",
    "\n",
    "- （1）森林中任意两棵树的相关性越大，错误率越大；\n",
    "- （2）减小子数据间的特征选择个数，树的相关性和分类能力也会相应的降低；增大特征个数，树的相关性和分类能力会相应的提高。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ★随机森林蕴含的思想:\n",
    "\n",
    "回顾随机森林学习模型的步骤：\n",
    "\n",
    "- （1）对原始数据集进行可放回随机抽样成K组子数据集；\n",
    "- （2）从样本的N个特征随机抽样m个特征；\n",
    "- （3）对每个子数据集构建最优学习模型\n",
    "- （4）对于新的输入数据，根据K个最优学习模型，得到最终结果。\n",
    "\n",
    "思想：\n",
    "\n",
    "- 步骤（1）是随机有放回抽取的，相关性大小具有随机性，因此，特征个数是优化随机森林模型的一个重要参数。\n",
    "- 步骤（2）的随机抽样的结果是子数据集间有不同的子特征，我们把不同的特征代表不同的领域，\n",
    "- 步骤（3）表示在不同领域学习到最顶尖的程度，\n",
    "- 步骤（4）表示对于某一个输入数据，用不同领域最顶尖的观点去看待输入数据，得到比较全面的结果。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机森林的模型估计方法\n",
    "\n",
    "- 对于包含m个样本的原始数据集，对该原始数据集进行可放回抽样m次，每次被采集到的概率是1/m，不被采集到的概率是(1-1/m)。m次采样不被抽到的概率是(1-1/m)^m\n",
    "\n",
    "- 当m→∞时，(1-1/m)^m → （1 / e）≈0.368 。因此在bagging的每轮抽样中，训练集大约有36.8%的数据没有被采样，这份数据称之为袋外数据（Out Of Bag，简称OOB）。\n",
    "\n",
    "- 模型的估计方法有两种:（1）袋外数据误差估计模型 （2）交叉验证率估计模型。\n",
    "  #### ★袋外数据(OOB)误差估计是一种可以取代测试集的误差估计方法，即袋外数据误差是测试数据集误差的无偏估计，因此可以用OOB数据用来检测模型的泛化能力。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 下面我们主要考虑的是：线性模型预测的效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- （1）我们采用train_test_split()将数据分为两份：训练集train，和测试集test；\n",
    "- （2）我们在train上训练模型，在test上计算模型得分（得分越靠近1，说明模型预测效果好）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19019457558086494"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LinearRegression()\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 这个得分0.19是怎么算出来的？\n",
    "\n",
    "- sklearn.linear_model.LinearRegression.score\n",
    "- 方法：score(self, X, y, sample_weight=None)\n",
    "\n",
    "#### 官方描述：Returns the coefficient of determination R^2 of the prediction:\n",
    "- The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.\n",
    "\n",
    "\n",
    "#### 作用：返回该次预测的系数R^2，其中R^2 =（1-u/v）。 u=((y_true - y_pred) ** 2).sum()   v=((y_true - y_true.mean()) ** 2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[32.        , 51.02192067],\n",
       "       [14.        , 30.40273297],\n",
       "       [28.        , 26.27889543],\n",
       "       [56.        , 63.39343329],\n",
       "       [ 4.        ,  9.78354526],\n",
       "       [40.        , 46.89808313],\n",
       "       [32.        , 63.39343329],\n",
       "       [42.        , 55.14575821],\n",
       "       [80.        , 38.65040805],\n",
       "       [16.        , 13.90738281],\n",
       "       [76.        , 55.14575821],\n",
       "       [36.        , 59.26959575],\n",
       "       [32.        , 46.89808313]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack((y_test.values.reshape(-1,1),model.predict(X_test).reshape(-1,1)))  #hstack作用：将两个数组按水平方向组合起来"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 我们再考虑：随机森林预测的效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3442774185832952"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr = RandomForestRegressor()\n",
    "regr.fit(X_train,y_train)\n",
    "regr.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机森林的得分为0.43，明显要比线性回归预测效果好"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机森林预测极其精准，唯二的缺点是：解释性不如回归模型，无法外推。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Input feature')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "line = np.linspace(0, 30, 100, endpoint=False).reshape(-1, 1)\n",
    "reg = RandomForestRegressor().fit(X, y)\n",
    "plt.plot(line, reg.predict(line), label=\"random_forest\")\n",
    "\n",
    "plt.plot(X, y, 'o', c='k')\n",
    "plt.ylabel(\"Forest output\")\n",
    "plt.xlabel(\"Input feature\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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